# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Dict
import numpy as np
import torch
from nvflare.app_common.abstract.params_converter import ParamsConverter
[docs]
class PTReceiveParamsConverter(ParamsConverter):
[docs]
def convert(self, params: Dict, fl_ctx) -> Dict:
tensor_shapes = fl_ctx.get_prop("tensor_shapes")
exclude_vars = fl_ctx.get_prop("exclude_vars")
return_params = {}
for k, v in params.items():
if isinstance(v, torch.Tensor):
return_params[k] = v
else:
# "PT receive, so potentially also need to handle numpy to tensor"
if tensor_shapes:
if k in tensor_shapes:
return_params[k] = torch.as_tensor(np.reshape(v, tensor_shapes[k]))
else:
return_params[k] = torch.as_tensor(v)
else:
return_params[k] = torch.as_tensor(v)
if exclude_vars:
for k, v in exclude_vars.items():
return_params[k] = v
return return_params
[docs]
class PTSendParamsConverter(ParamsConverter):
[docs]
def convert(self, params: Dict, fl_ctx) -> Dict:
return_tensors = {}
exclude_vars = {}
for k, v in params.items():
if isinstance(v, torch.Tensor):
return_tensors[k] = v.cpu()
else:
exclude_vars[k] = v
if exclude_vars:
fl_ctx.set_prop("exclude_vars", exclude_vars)
self.logger.warning(
f"{len(exclude_vars)} vars excluded as they were non-tensor type: " f"{list(exclude_vars.keys())}"
)
return return_tensors